

import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
__snake_case				:      int   		=			logging.get_logger(__name__)


def 					_UpperCAmelCase				(						a__  ,	a__=False):

			'''simple docstring'''



			a_						:						Tuple 							= []

			# fmt: off
			# stem:
			rename_keys.append(("""cls_token""", """vit.embeddings.cls_token"""))
			rename_keys.append(("""pos_embed""", """vit.embeddings.position_embeddings"""))

			rename_keys.append(("""patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight"""))
			rename_keys.append(("""patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias"""))

			# backbone
			rename_keys.append(("""patch_embed.backbone.stem.conv.weight""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight"""))
			rename_keys.append(("""patch_embed.backbone.stem.norm.weight""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight"""))
			rename_keys.append(("""patch_embed.backbone.stem.norm.bias""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias"""))

			for stage_idx in range(len(config.backbone_config.depths)):
						for layer_idx in range(config.backbone_config.depths[stage_idx]):
									rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight'''))
									rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight'''))
									rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias'''))
									rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight'''))
									rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight'''))
									rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias'''))
									rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight'''))
									rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight'''))
									rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias'''))

						rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight'''))
						rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight'''))
						rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias'''))

			# transformer encoder
			for i in range(config.num_hidden_layers):
						# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
						rename_keys.append((f'''blocks.{i}.norm1.weight''', f'''vit.encoder.layer.{i}.layernorm_before.weight'''))
						rename_keys.append((f'''blocks.{i}.norm1.bias''', f'''vit.encoder.layer.{i}.layernorm_before.bias'''))
						rename_keys.append((f'''blocks.{i}.attn.proj.weight''', f'''vit.encoder.layer.{i}.attention.output.dense.weight'''))
						rename_keys.append((f'''blocks.{i}.attn.proj.bias''', f'''vit.encoder.layer.{i}.attention.output.dense.bias'''))
						rename_keys.append((f'''blocks.{i}.norm2.weight''', f'''vit.encoder.layer.{i}.layernorm_after.weight'''))
						rename_keys.append((f'''blocks.{i}.norm2.bias''', f'''vit.encoder.layer.{i}.layernorm_after.bias'''))
						rename_keys.append((f'''blocks.{i}.mlp.fc1.weight''', f'''vit.encoder.layer.{i}.intermediate.dense.weight'''))
						rename_keys.append((f'''blocks.{i}.mlp.fc1.bias''', f'''vit.encoder.layer.{i}.intermediate.dense.bias'''))
						rename_keys.append((f'''blocks.{i}.mlp.fc2.weight''', f'''vit.encoder.layer.{i}.output.dense.weight'''))
						rename_keys.append((f'''blocks.{i}.mlp.fc2.bias''', f'''vit.encoder.layer.{i}.output.dense.bias'''))

			if base_model:
						# layernorm + pooler
						rename_keys.extend(
						    [
						        ("""norm.weight""", """layernorm.weight"""),
						        ("""norm.bias""", """layernorm.bias"""),
						        ("""pre_logits.fc.weight""", """pooler.dense.weight"""),
						        ("""pre_logits.fc.bias""", """pooler.dense.bias"""),
						    ])

						# if just the base model, we should remove "vit" from all keys that start with "vit"
						a_						:						Optional[int] 							= [(pair[0], pair[1][4:]) if pair[1].startswith("""vit""") else pair for pair in rename_keys]
			else:
						# layernorm + classification head
						rename_keys.extend(
						    [
						        ("""norm.weight""", """vit.layernorm.weight"""),
						        ("""norm.bias""", """vit.layernorm.bias"""),
						        ("""head.weight""", """classifier.weight"""),
						        ("""head.bias""", """classifier.bias"""),
						    ])
			# fmt: on

			return rename_keys


def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__=False):

			'''simple docstring'''



			for i in range(config.num_hidden_layers):
						if base_model:
									a_						:						Union[str, Any] 							= """"""
						else:
									a_						:						List[Any] 							= """vit."""
						# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
						a_						:						Any 							= state_dict.pop(f'''blocks.{i}.attn.qkv.weight''')
						a_						:						List[Any] 							= state_dict.pop(f'''blocks.{i}.attn.qkv.bias''')
						# next, add query, keys and values (in that order) to the state dict
						a_						:						Optional[Any] 							= in_proj_weight[
						    : config.hidden_size, :
						]
						a_						:						Union[str, Any] 							= in_proj_bias[: config.hidden_size]
						a_						:						Optional[int] 							= in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						a_						:						Any 							= in_proj_bias[
						    config.hidden_size : config.hidden_size * 2
						]
						a_						:						Union[str, Any] 							= in_proj_weight[
						    -config.hidden_size :, :
						]
						a_						:						Optional[int] 							= in_proj_bias[-config.hidden_size :]


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						Any 							= ["""head.weight""", """head.bias"""]
			for k in ignore_keys:
						state_dict.pop(a__  ,	a__)


def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__):

			'''simple docstring'''



			a_						:						Any 							= dct.pop(a__)
			a_						:						int 							= val


def 					_UpperCAmelCase				(						):

			'''simple docstring'''



			a_						:						Any 							= """http://images.cocodataset.org/val2017/000000039769.jpg"""
			a_						:						Optional[Any] 							= Image.open(requests.get(a__  ,	stream=a__).raw)
			return im


@torch.no_grad()
def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__=False):

			'''simple docstring'''



			a_						:						List[str] 							= BitConfig(
			    global_padding="""same"""  ,	layer_type="""bottleneck"""  ,	depths=(3, 4, 9)  ,	out_features=["""stage3"""]  ,	embedding_dynamic_padding=a__  ,	)
			a_						:						Any 							= ViTHybridConfig(backbone_config=a__  ,	image_size=3_8_4  ,	num_labels=1_0_0_0)
			a_						:						Union[str, Any] 							= False

			# load original model from timm
			a_						:						Dict 							= timm.create_model(a__  ,	pretrained=a__)
			timm_model.eval()

			# load state_dict of original model, remove and rename some keys
			a_						:						Dict 							= timm_model.state_dict()
			if base_model:
						remove_classification_head_(a__)
			a_						:						List[str] 							= create_rename_keys(a__  ,	a__)
			for src, dest in rename_keys:
						rename_key(a__  ,	a__  ,	a__)
			read_in_q_k_v(a__  ,	a__  ,	a__)

			a_						:						str 							= """huggingface/label-files"""
			a_						:						Any 							= """imagenet-1k-id2label.json"""
			a_						:						Optional[Any] 							= json.load(open(hf_hub_download(a__  ,	a__  ,	repo_type="""dataset""")  ,	"""r"""))
			a_						:						Optional[int] 							= {int(a__): v for k, v in idalabel.items()}
			a_						:						Optional[Any] 							= idalabel
			a_						:						str 							= {v: k for k, v in idalabel.items()}

			# load HuggingFace model
			if vit_name[-5:] == "in21k":
						a_						:						List[str] 							= ViTHybridModel(a__).eval()
			else:
						a_						:						Optional[Any] 							= ViTHybridForImageClassification(a__).eval()
			model.load_state_dict(a__)

			# create image processor
			a_						:						Optional[Any] 							= create_transform(**resolve_data_config({}  ,	model=a__))
			a_						:						int 							= transform.transforms

			a_						:						str 							= {
			    """bilinear""": PILImageResampling.BILINEAR,
			    """bicubic""": PILImageResampling.BICUBIC,
			    """nearest""": PILImageResampling.NEAREST,
			}

			a_						:						Dict 							= ViTHybridImageProcessor(
			    do_resize=a__  ,	size={"""shortest_edge""": timm_transforms[0].size}  ,	resample=pillow_resamplings[timm_transforms[0].interpolation.value]  ,	do_center_crop=a__  ,	crop_size={"""height""": timm_transforms[1].size[0], """width""": timm_transforms[1].size[1]}  ,	do_normalize=a__  ,	image_mean=timm_transforms[-1].mean.tolist()  ,	image_std=timm_transforms[-1].std.tolist()  ,	)

			a_						:						Tuple 							= prepare_img()
			a_						:						Any 							= transform(a__).unsqueeze(0)
			a_						:						Any 							= processor(a__  ,	return_tensors="""pt""").pixel_values

			# verify pixel values
			assert torch.allclose(a__  ,	a__)

			# verify logits
			with torch.no_grad():
						a_						:						Tuple 							= model(a__)
						a_						:						str 							= outputs.logits

			print("""Predicted class:"""  ,	logits.argmax(-1).item())
			if base_model:
						a_						:						List[Any] 							= timm_model.forward_features(a__)
						assert timm_pooled_output.shape == outputs.pooler_output.shape
						assert torch.allclose(a__  ,	outputs.pooler_output  ,	atol=1e-3)
			else:
						a_						:						Any 							= timm_model(a__)
						assert timm_logits.shape == outputs.logits.shape
						assert torch.allclose(a__  ,	outputs.logits  ,	atol=1e-3)
			print("""Looks ok!""")

			if pytorch_dump_folder_path is not None:
						Path(a__).mkdir(exist_ok=a__)
						print(f'''Saving model {vit_name} to {pytorch_dump_folder_path}''')
						model.save_pretrained(a__)
						print(f'''Saving processor to {pytorch_dump_folder_path}''')
						processor.save_pretrained(a__)

			if push_to_hub:
						print(f'''Pushing model and processor to the hub {vit_name}''')
						model.push_to_hub(f'''ybelkada/{vit_name}''')
						processor.push_to_hub(f'''ybelkada/{vit_name}''')


if __name__ == "__main__":
	__snake_case				:      List[Any]   		=			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--vit_name""",
	    default="""vit_base_r50_s16_384""",
	    type=str,
	    help="""Name of the hybrid ViT timm model you'd like to convert.""",
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
	)
	parser.add_argument(
	    """--push_to_hub""", action="""store_true""", help="""Whether to upload the model to the HuggingFace hub."""
	)

	__snake_case				:      Dict   		=			parser.parse_args()
	convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)





import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class        A__(a_,  a_,  unittest.TestCase							):
						"""simple docstring"""






						_A    :				Optional[Any]       					=			StableDiffusionSAGPipeline
						_A    :				Any       					=			TEXT_TO_IMAGE_PARAMS
						_A    :				Dict       					=			TEXT_TO_IMAGE_BATCH_PARAMS
						_A    :				Dict       					=			TEXT_TO_IMAGE_IMAGE_PARAMS
						_A    :				int       					=			TEXT_TO_IMAGE_IMAGE_PARAMS
						_A    :				Optional[int]       					=			False





						def 			UpperCamelCase__					(    self    )     -> List[Any]:
									torch.manual_seed(0    )
									a_						:						Tuple 							= UNetaDConditionModel(
									    block_out_channels=(32, 64)  ,      layers_per_block=2  ,      sample_size=32  ,      in_channels=4  ,      out_channels=4  ,      down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")  ,      up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")  ,      cross_attention_dim=32  ,      )
									a_						:						int 							= DDIMScheduler(
									    beta_start=0.0_0_0_8_5  ,      beta_end=0.0_1_2  ,      beta_schedule="""scaled_linear"""  ,      clip_sample=_lowercase  ,      set_alpha_to_one=_lowercase  ,      )
									torch.manual_seed(0    )
									a_						:						Tuple 							= AutoencoderKL(
									    block_out_channels=[32, 64]  ,      in_channels=3  ,      out_channels=3  ,      down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]  ,      up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]  ,      latent_channels=4  ,      )
									torch.manual_seed(0    )
									a_						:						str 							= CLIPTextConfig(
									    bos_token_id=0  ,      eos_token_id=2  ,      hidden_size=32  ,      intermediate_size=37  ,      layer_norm_eps=1e-05  ,      num_attention_heads=4  ,      num_hidden_layers=5  ,      pad_token_id=1  ,      vocab_size=1_000  ,      )
									a_						:						Union[str, Any] 							= CLIPTextModel(_lowercase    )
									a_						:						int 							= CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )

									a_						:						Union[str, Any] 							= {
									    """unet""": unet,
									    """scheduler""": scheduler,
									    """vae""": vae,
									    """text_encoder""": text_encoder,
									    """tokenizer""": tokenizer,
									    """safety_checker""": None,
									    """feature_extractor""": None,
									}
									return components





						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase=0    )     -> Dict:
									if str(_lowercase    ).startswith("""mps"""    ):
												a_						:						Optional[int] 							= torch.manual_seed(_lowercase    )
									else:
												a_						:						str 							= torch.Generator(device=_lowercase    ).manual_seed(_lowercase    )
									a_						:						Tuple 							= {
									    """prompt""": """.""",
									    """generator""": generator,
									    """num_inference_steps""": 2,
									    """guidance_scale""": 1.0,
									    """sag_scale""": 1.0,
									    """output_type""": """numpy""",
									}
									return inputs





						def 			UpperCamelCase__					(    self    )     -> Optional[int]:
									super().test_inference_batch_single_identical(expected_max_diff=3e-3    )







@slow
@require_torch_gpu
class        A__(unittest.TestCase							):
						"""simple docstring"""






						def 			UpperCamelCase__					(    self    )     -> Optional[int]:
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()





						def 			UpperCamelCase__					(    self    )     -> Optional[int]:
									a_						:						Tuple 							= StableDiffusionSAGPipeline.from_pretrained("""CompVis/stable-diffusion-v1-4"""    )
									a_						:						List[str] 							= sag_pipe.to(_lowercase    )
									sag_pipe.set_progress_bar_config(disable=_lowercase    )

									a_						:						Optional[int] 							= """."""
									a_						:						int 							= torch.manual_seed(0    )
									a_						:						Any 							= sag_pipe(
									    [prompt]  ,      generator=_lowercase  ,      guidance_scale=7.5  ,      sag_scale=1.0  ,      num_inference_steps=20  ,      output_type="""np"""    )

									a_						:						Any 							= output.images

									a_						:						Optional[int] 							= image[0, -3:, -3:, -1]

									assert image.shape == (1, 512, 512, 3)
									a_						:						str 							= np.array([0.1_5_6_8, 0.1_7_3_8, 0.1_6_9_5, 0.1_6_9_3, 0.1_5_0_7, 0.1_7_0_5, 0.1_5_4_7, 0.1_7_5_1, 0.1_9_4_9]    )

									assert np.abs(image_slice.flatten() - expected_slice    ).max() < 5e-2





						def 			UpperCamelCase__					(    self    )     -> List[Any]:
									a_						:						Optional[int] 							= StableDiffusionSAGPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base"""    )
									a_						:						List[str] 							= sag_pipe.to(_lowercase    )
									sag_pipe.set_progress_bar_config(disable=_lowercase    )

									a_						:						int 							= """."""
									a_						:						Dict 							= torch.manual_seed(0    )
									a_						:						Union[str, Any] 							= sag_pipe(
									    [prompt]  ,      generator=_lowercase  ,      guidance_scale=7.5  ,      sag_scale=1.0  ,      num_inference_steps=20  ,      output_type="""np"""    )

									a_						:						Optional[Any] 							= output.images

									a_						:						int 							= image[0, -3:, -3:, -1]

									assert image.shape == (1, 512, 512, 3)
									a_						:						Dict 							= np.array([0.3_4_5_9, 0.2_8_7_6, 0.2_5_3_7, 0.3_0_0_2, 0.2_6_7_1, 0.2_1_6_0, 0.3_0_2_6, 0.2_2_6_2, 0.2_3_7_1]    )

									assert np.abs(image_slice.flatten() - expected_slice    ).max() < 5e-2





						def 			UpperCamelCase__					(    self    )     -> Any:
									a_						:						int 							= StableDiffusionSAGPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base"""    )
									a_						:						Optional[Any] 							= sag_pipe.to(_lowercase    )
									sag_pipe.set_progress_bar_config(disable=_lowercase    )

									a_						:						List[Any] 							= """."""
									a_						:						str 							= torch.manual_seed(0    )
									a_						:						int 							= sag_pipe(
									    [prompt]  ,      width=768  ,      height=512  ,      generator=_lowercase  ,      guidance_scale=7.5  ,      sag_scale=1.0  ,      num_inference_steps=20  ,      output_type="""np"""  ,      )

									a_						:						Any 							= output.images

									assert image.shape == (1, 512, 768, 3)


